todo-files/StackResults/boot_classifStack.R

In mlr-org/mlr: Machine Learning in R

library(devtools)
library(mlr)
# load_all("mlr")
library(colorspace)
library("parallelMap")
configureMlr(show.learner.output = FALSE, on.learner.warning = "quiet", show.info = FALSE,
  on.par.without.desc = "quiet")
parallelStartMulticore(15)
set.seed(123)
task = sonar.task

# settings
sm.pt = "prob"
bms.pt = "prob"
classifMeasure = list("auc" = mlr:::auc, "acc" = acc, "timetrain" = timetrain)
rin = makeResampleInstance("Bootstrap", iters = 30, task = task)
methods = c("stack.cv", "stack.nocv", "average")
super = makeLearner("classif.randomForest", predict.type = sm.pt)

# omit "slow" classif learners
classl = listLearners(obj = "classif", properties = c("factors", "prob"))
classl = classl[!grepl(".crs|.km|.mob|.nnet|.rvm|.IBk|.boosting|.randomForestSRC|.mda|.qda|.rda", classl)]
# classl <- c("classif.kknn", "classif.ksvm", "classif.svm")

# fit base learners and select only the 50% best base learners
base = lapply(as.list(classl), function(X) makeLearner(X, predict.type = bms.pt))
baseMod = vector("list", length(base))
for (i in 1:length(base)) {
  baseMod[[i]] = mlr:::resample(base[[i]], task, resampling = rin, measures = classifMeasure)
  cat(classl[i], " finished", fill = TRUE)
}

# determine best AUC models
baseModAUC = do.call("cbind", lapply(baseMod, function(X) X$measures.test[, "auc"]))
colnames(baseModAUC) = sapply(base, function(X) X$id)
medAUC = apply(baseModAUC, 2, median)

bestAUC = baseModAUC[, medAUC > quantile(medAUC, 0.5)]
pdf("classifBaseLearner.pdf")
boxplot(baseModAUC, las = 2, main = "AUC")
abline(h = quantile(medAUC, 0.5), col = "gray")
dev.off()
base = lapply(as.list(colnames(bestAUC)), function(X) makeLearner(X, predict.type = bms.pt))

# Fit stacked learner without original features
slrn = lapply(methods, function(X) {
  if (X == "average") {
    lrn = makeStackedLearner(base.learners = base, method = X,
      predict.type = sm.pt)
    # lrn = setPredictType(lrn, predict.type = sm.pt)
    return(lrn)
  } else {
    makeStackedLearner(base.learners = base,
      super.learner = super,
      method = X)
  }
})
names(slrn) = methods

# use original features
slrnFeat = lapply(methods, function(X) {
  if (X != "average") {
    makeStackedLearner(base.learners = base,
      super.learner = super,
      method = X, use.feat = TRUE)
  }
})
rmNull = !sapply(slrnFeat, is.null)
names(slrnFeat) = methods[rmNull]
slrnFeat = slrnFeat[rmNull]

res = lapply(slrn, function(X) mlr:::resample(X, task, resampling = rin, measures = classifMeasure))
resFeat = lapply(slrnFeat, function(X) mlr:::resample(X, task, resampling = rin, measures = classifMeasure))
parallelStop()

resAUC = do.call("cbind", lapply(res, function(X) X$measures.test[, "auc"]))
resFeatAUC = do.call("cbind", lapply(resFeat, function(X) X$measures.test[, "auc"]))

pdf("classifStack_auc.pdf")
par(mar = c(10, 4, 2, 2))
hlc = rainbow_hcl(3)
cols = c(rep(hlc[1], ncol(resAUC)), rep(hlc[2], ncol(resFeatAUC)), rep(hlc[3], ncol(bestAUC)))
aucs = cbind(resAUC, resFeatAUC, bestAUC)
ind = order(apply(aucs, 2, median), decreasing = TRUE)
boxplot(aucs[, ind], las = 2, col = cols[ind], main = "AUC")
legend("bottomleft", col = hlc, pch = 15,
  legend = c("stack without features", "stack with features", "base learners"))
dev.off()

# lets take a look at ACC
baseModACC = do.call("cbind", lapply(baseMod, function(X) X$measures.test[, "acc"]))
colnames(baseModACC) = colnames(baseModAUC)
medACC = apply(baseModACC, 2, median)
bestACC = baseModACC[, medACC > quantile(medACC, 0.5)]
resACC = do.call("cbind", lapply(res, function(X) X$measures.test[, "acc"]))
resFeatACC = do.call("cbind", lapply(resFeat, function(X) X$measures.test[, "acc"]))

pdf("classifStack_acc.pdf")
par(mar = c(10, 4, 2, 2))
hlc = rainbow_hcl(3)
cols = c(rep(hlc[1], ncol(resACC)), rep(hlc[2], ncol(resFeatACC)), rep(hlc[3], ncol(bestACC)))
accs = cbind(resACC, resFeatACC, bestACC)
ind = order(apply(accs, 2, median), decreasing = TRUE)
boxplot(accs[, ind], las = 2, col = cols[ind], main = "ACC")
legend("bottomleft", col = hlc, pch = 15,
  legend = c("stack without features", "stack with features", "base learners"))
dev.off()

save.image(file = "classifStack.RData")